Comparing Missing Data Methods for Estimating Average Treatment Effects Under Time-Varying Confounding: A Simulation Study

2026-07-20

AI Summary

This study systematically investigates how missingness mechanisms, missing rates, missingness locations, and sample sizes jointly affect the accuracy and identifiability of average treatment effect (ATE) estimation under time-varying confounding. Through simulation, it compares the performance of complete-case analysis, stratified hot-deck imputation, single-model imputation, and multiple imputation by chained equations (MICE) combined with propensity score weighting, offering the first comprehensive assessment of their interactive effects. The results demonstrate that multiple imputation substantially reduces bias and improves confidence interval coverage across most scenarios. Crucially, the missingness mechanism emerges as a key determinant of estimator performance: missing not at random (MNAR) conditions, high missing rates, or small sample sizes frequently violate the positivity assumption, thereby undermining estimation validity.

Abstract

Missing data and confounding are common in real-world statistical applications, yet few studies have examined how imputation methods perform under time-varying confounding in binary variables, or how missingness mechanism, missing rate, missingness location and sample size jointly affect performance and the underlying identifiability conditions. We generated synthetic data and conducted a simulation study comparing missing data methods across scenarios varying these factors. Missingness was introduced in both treatment and outcome variables, and we applied stratified hot deck imputation, single mode imputation, multiple imputation with chained equations (MICE), and complete-case analysis. Average treatment effect (ATE) estimates were obtained using logistic regression with propensity score weighting, and we measured coverage, absolute bias and empirical standard errors across 48 scenarios with 500 replications each. Performance was primarily driven by the missingness mechanism and choice of method, with multiple imputation generally achieving better coverage and lower bias than other methods. Missingness location was also important, while missing rate and sample size primarily affected positivity violations, which were most pronounced under MNAR, high missingness and low sample sizes. Exchangeability violations from mild to moderate confounding were adequately controlled for by propensity score models, whereas strong confounding produced a modest decrease in coverage. Further research should examine additional ways identifiability conditions can be violated under missingness, using more advanced methods and more complex missingness scenarios.

Problem

• missing data
• time-varying confounding
• average treatment effect
• identifiability
• simulation study

Innovation

• time-varying confounding
• missing data imputation
• average treatment effect
• multiple imputation
• propensity score weighting